Detecting and Monitoring Hate Speech in Twitter

Social Media are sensors in the real world that can be used to measure the pulse of societies. However, the massive and unfiltered feed of messages posted in social media is a phenomenon that nowadays raises social alarms, especially when these messages contain hate speech targeted to a specific ind...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 19; no. 21; p. 4654
Main Authors: Pereira-Kohatsu, Juan Carlos, Quijano-Sánchez, Lara, Liberatore, Federico, Camacho-Collados, Miguel
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 26.10.2019
MDPI
Subjects:
Classification
Datasets
hate crime
Hate speech
Neural networks
predictive policing
sentiment analysis
social network analysis
Social networks
text classification
twitter
Twitter
hate crime
text classification
sentiment analysis
predictive policing
social network analysis
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:Social Media are sensors in the real world that can be used to measure the pulse of societies. However, the massive and unfiltered feed of messages posted in social media is a phenomenon that nowadays raises social alarms, especially when these messages contain hate speech targeted to a specific individual or group. In this context, governments and non-governmental organizations (NGOs) are concerned about the possible negative impact that these messages can have on individuals or on the society. In this paper, we present HaterNet, an intelligent system currently being used by the Spanish National Office Against Hate Crimes of the Spanish State Secretariat for Security that identifies and monitors the evolution of hate speech in Twitter. The contributions of this research are many-fold: (1) It introduces the first intelligent system that monitors and visualizes, using social network analysis techniques, hate speech in Social Media. (2) It introduces a novel public dataset on hate speech in Spanish consisting of 6000 expert-labeled tweets. (3) It compares several classification approaches based on different document representation strategies and text classification models. (4) The best approach consists of a combination of a LTSM+MLP neural network that takes as input the tweet’s word, emoji, and expression tokens’ embeddings enriched by the tf-idf, and obtains an area under the curve (AUC) of 0.828 on our dataset, outperforming previous methods presented in the literature.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s19214654